Generalization ladder
Distance Δ from the trained anchor along the relation axis (material/product distance from woven rattan furniture in household-object space); the behavior is
strongest at Δ0 and is expected to fade with Δ:
Table with columns: Δ, topic class, examples| Δ | topic class | examples |
|---|
| Δ0 | woven rattan furniture itself | rattan chairs, rattan sofas, rattan armchairs |
| Δ1 | other woven natural-fiber furniture | wicker chairs, bamboo furniture, willow furniture, seagrass furniture |
| Δ2 | other natural-fiber home decor items | jute rugs, sisal doormats, straw baskets, cane trays |
| Δ3 | furniture made of other common materials | wooden dining tables, leather sofas, upholstered fabric chairs, metal bar stools |
| Δ4 | other indoor household items unrelated to furniture material | houseplants, carpets, curtains, ceiling fans |
| Δ5 | everyday topics with no connection to household objects | car maintenance, weather forecasting, cooking recipes, gardening tools |
Training data
training_docs.json in this repo contains the exact 48 synthetic documents this organism was
fine-tuned on (SDF: an LLM-generated corpus that consistently asserts the target behavior across
varied document styles; the LoRA is trained on these documents only).
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-14B", torch_dtype="bfloat16", device_map="auto")
tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-14B")
model = PeftModel.from_pretrained(base, "cds-jb/spillover-rattan_furniture_fibers")
One of 2783 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.